






'''simple docstring'''





import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_UpperCamelCase						:						str  =					{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_UpperCamelCase						:						Any  =					{
    '169M': 768,
    '430M': 1_024,
    '1B5': 2_048,
    '3B': 2_560,
    '7B': 4_096,
    '14B': 5_120,
}
def __UpperCAmelCase   (					A					:			Any		)    ->   List[str]:
			UpperCAmelCase_      :       Union[str, Any]          =       list(state_dict.keys()		)
			for name in state_dict_keys:
						UpperCAmelCase_      :       Union[str, Any]          =       state_dict.pop(A		)
						# emb -> embedding
						if name.startswith('''emb.'''		):
									UpperCAmelCase_      :       str          =       name.replace('''emb.'''			,       '''embeddings.'''		)
						# ln_0 -> pre_ln (only present at block 0)
						if name.startswith('''blocks.0.ln0'''		):
									UpperCAmelCase_      :       Tuple          =       name.replace('''blocks.0.ln0'''			,       '''blocks.0.pre_ln'''		)
						# att -> attention
						UpperCAmelCase_      :       Optional[int]          =       re.sub(r'''blocks\.(\d+)\.att'''			,       r'''blocks.\1.attention'''			,       A		)
						# ffn -> feed_forward
						UpperCAmelCase_      :       List[Any]          =       re.sub(r'''blocks\.(\d+)\.ffn'''			,       r'''blocks.\1.feed_forward'''			,       A		)
						# time_mix_k -> time_mix_key and reshape
						if name.endswith('''.time_mix_k'''		):
									UpperCAmelCase_      :       Optional[int]          =       name.replace('''.time_mix_k'''			,       '''.time_mix_key'''		)
						# time_mix_v -> time_mix_value and reshape
						if name.endswith('''.time_mix_v'''		):
									UpperCAmelCase_      :       Tuple          =       name.replace('''.time_mix_v'''			,       '''.time_mix_value'''		)
						# time_mix_r -> time_mix_key and reshape
						if name.endswith('''.time_mix_r'''		):
									UpperCAmelCase_      :       List[Any]          =       name.replace('''.time_mix_r'''			,       '''.time_mix_receptance'''		)

						if name != "head.weight":
									UpperCAmelCase_      :       int          =       '''rwkv.''' + name

						UpperCAmelCase_      :       List[str]          =       weight
			return state_dict
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			int			,       A					:			Union[str, Any]			,       A					:			Optional[int]=None			,       A					:			Union[str, Any]=None			,       A					:			int=False			,       A					:			Tuple=None		)    ->   int:
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''		)
						UpperCAmelCase_      :       Tuple          =       5_0_2_7_7
						UpperCAmelCase_      :       Dict          =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''		)
			else:
						UpperCAmelCase_      :       Optional[int]          =       PreTrainedTokenizerFast(tokenizer_file=A		)
						UpperCAmelCase_      :       List[Any]          =       len(A		)
			tokenizer.save_pretrained(A		)

			# 2. Build the config
			UpperCAmelCase_      :       Dict          =       list(NUM_HIDDEN_LAYERS_MAPPING.keys()		)
			if size is None:
						# Try to infer size from the checkpoint name
						for candidate in possible_sizes:
									if candidate in checkpoint_file:
												UpperCAmelCase_      :       Dict          =       candidate
												break
						if size is None:
									raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''		)
			if size not in possible_sizes:
						raise ValueError(F"`size` should be one of {possible_sizes}, got {size}."		)

			UpperCAmelCase_      :       Optional[int]          =       RwkvConfig(
			    vocab_size=A			,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,       hidden_size=HIDEN_SIZE_MAPPING[size]			,       )
			config.save_pretrained(A		)

			# 3. Download model file then convert state_dict
			UpperCAmelCase_      :       Union[str, Any]          =       hf_hub_download(A			,       A		)
			UpperCAmelCase_      :       List[Any]          =       torch.load(A			,       map_location='''cpu'''		)
			UpperCAmelCase_      :       str          =       convert_state_dict(A		)

			# 4. Split in shards and save
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       shard_checkpoint(A		)
			for shard_file, shard in shards.items():
						torch.save(A			,       os.path.join(A			,       A		)		)

			if index is not None:
						UpperCAmelCase_      :       List[str]          =       os.path.join(A			,       A		)
						# Save the index as well
						with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
									UpperCAmelCase_      :       Dict          =       json.dumps(A			,       indent=2			,       sort_keys=A		) + '''\n'''
									f.write(A		)

						# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
						print(
						    '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''		)
						UpperCAmelCase_      :       List[Any]          =       list(shards.keys()		)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									UpperCAmelCase_      :       List[Any]          =       torch.load(os.path.join(A			,       A		)		)
									torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,       os.path.join(A			,       A		)		)

			del state_dict
			gc.collect()

			if push_to_hub:
						if model_name is None:
									raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''		)
						UpperCAmelCase_      :       Optional[Any]          =       AutoModelForCausalLM.from_pretrained(A		)
						model.push_to_hub(A			,       max_shard_size='''2GB'''		)
						tokenizer.push_to_hub(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						str  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
						)
						parser.add_argument(
						    '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
						)
						parser.add_argument(
						    '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
						)
						parser.add_argument(
						    '--tokenizer_file',
						    default=None,
						    type=str,
						    help='Path to the tokenizer file to use (if not provided, only the model is converted).',
						)
						parser.add_argument(
						    '--size',
						    default=None,
						    type=str,
						    help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Push to the Hub the converted model.',
						)
						parser.add_argument(
						    '--model_name',
						    default=None,
						    type=str,
						    help='Name of the pushed model on the Hub, including the username / organization.',
						)

						_UpperCamelCase						:						Optional[Any]  =					parser.parse_args()
						convert_rmkv_checkpoint_to_hf_format(
						    args.repo_id,
						    args.checkpoint_file,
						    args.output_dir,
						    size=args.size,
						    tokenizer_file=args.tokenizer_file,
						    push_to_hub=args.push_to_hub,
						    model_name=args.model_name,
						)










'''simple docstring'''





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       []
			for part_id in partition_order:
						UpperCAmelCase_      :       Any          =       df.where(F"SPARK_PARTITION_ID() = {part_id}"		).collect()
						for row_idx, row in enumerate(A		):
									expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())		)
			return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       Any          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       List[Any]          =       Spark(A		)
			# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
			# that each partition can hold 2 rows.
			spark_builder._repartition_df_if_needed(max_shard_size=1_6		)
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       List[str]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Optional[Any]          =       spark.range(1_0		).repartition(2		)
			UpperCAmelCase_      :       int          =       [1, 0]
			UpperCAmelCase_      :       str          =       _generate_iterable_examples(A			,       A		)  # Reverse the partitions.
			UpperCAmelCase_      :       Optional[Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       A		)

			for i, (row_id, row_dict) in enumerate(generate_fn()		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       expected_row_ids_and_row_dicts[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       str          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[Any]          =       spark.range(1_0		).repartition(1		)
			UpperCAmelCase_      :       Optional[int]          =       SparkExamplesIterable(A		)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(A		):
						assert row_id == F"0_{i}"
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Dict          =       spark.range(3_0		).repartition(3		)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''		) as generator_mock:
						UpperCAmelCase_      :       Any          =       lambda A		: x.reverse()
						UpperCAmelCase_      :       Dict          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [2, 1, 0]		)

						UpperCAmelCase_      :       List[Any]          =       SparkExamplesIterable(A		).shuffle_data_sources(A		)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(A		):
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts[i]
									assert row_id == expected_row_id
									assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       Tuple          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       int          =       spark.range(2_0		).repartition(4		)

			# Partitions 0 and 2
			UpperCAmelCase_      :       int          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=0			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       Union[str, Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [0, 2]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict

			# Partitions 1 and 3
			UpperCAmelCase_      :       Tuple          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=1			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       List[str]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [1, 3]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       int          =       Spark(A		)
			# Choose a small max_shard_size for maximum partitioning.
			spark_builder._repartition_df_if_needed(max_shard_size=1		)
			# The new number of partitions should not be greater than the number of rows.
			assert spark_builder.df.rdd.getNumPartitions() == 1_0_0


